Analisis exploratorio: violencia criminal y trasiego de drogas
MIDE: DIPLOMADO EN CIENCIA DE DATOS
Karla Cruz Martínez
2023-12-12
rm(list = ls())
library(htmltools)
library(htmlwidgets)
library(AER)
library(plm)
library(CGPfunctions)
library(foreign)
library(gplots)
library(haven)
library(dplyr) # wickham2020b
library(DT) # xie2020
library(ggplot2) # wickham2016
library(Hmisc) # harrell2020
library(kableExtra) # zhu2021
library(knitr) # xie2014
library(RColorBrewer) # neuwirth2014
library(reshape2) # wickham2007
library(scales)
library(readxl)
library(GGally)
library(Hmisc)
library(corrplot)
library(PerformanceAnalytics)# wickham2020INTRODUCCIÓN
Actualmente México vive una ola de violencia. De acuerdo con algunos especialista, esto tiene relación con la presencia de grupos criminales y las políticas pública ejercida contra los mismos. Bajo este panorama, se pretende responder a ¿cómo es el comportamiento del crimen en México? ¿Los homicidios tienen un comportamiento generalizado en todo el país? ¿Cuáles son las áreas geográficas con mayores tasas de homicidios por armas de fuego? ¿Cómo han variado las tasas de homicidios a lo largo del tiempo entre hombres y mujeres? ¿Existe una relación entre el trasiego de drogas y los homicidios a nivel entidad federativa?
ESTABLECER EL CONTEXTO
Las actividades del crimen organizado tienen un impacto significativo en las comunidades y en los países, lo que ha llevado a diversos estados a combatirlo. Una de estas actividades es el mercado de drogas ilegales, el cual se encuentra en manos de organizaciones criminales desde su surgimiento a mitad del siglo XIX, ya que hay en juego prolíferos dividendos. En 2017, Global Financial Integrity estimó las ganancias totales anuales de once mercados delcrimen organizado, siendo el mercado de sustancias psicoactivas el segundo más lucrativo. Las ganancias calculadas oscilaron entre 426 mil millones y 652 mil millones de dólares anuales. Algunas de las razones por las cuales se origina dicho mercado de drogas ilegales, como coloquialmente se conoce, es la alta demanda de los países desarrollados y las limitadas condiciones socioeconómicas en los países productores, en donde hay un dominio de las organizaciones criminales. En 2017, el consumo de drogas aumentó 30 % con respecto al 2009. En especial incrementó el consumo de opioides, sustancias que derivan del jugo de la amapola o adormidera Papaver somniferum. El consumo de estas sustancias creció 56 % respecto al 2016; la heroína y el opio fueron las drogas más usadas. Aunado a esto, en las últimas dos décadas, la producción a gran escala de cultivos de amapola ha estado en ascenso en los principales países productores como son: Afganistán, Myanmar y México4 según United Nations Office on Drugs and Crime (UNODC) (2019). En el caso de México, entre 2015 y 2017, se registró una expansión del cultivo de amapola del 21 % en diversas zonas del país, de acuerdo con las cifras que presentó UNODC en 2018. Esta expansión podría estár relacionada con la alta demanda de opioides5 por Estados Unidos. Evans W., Lieber, E. y Power, P. (2019), documentan que, en 2010, Estados Unidos reportó un incremento de muertes por sobredosis de heroína como consecuencia de la reformulación de OxyContin , la cual buscaba evitar el abuso de opioides recetados. Por un lado, esta medida bien intencionada condujo a una serie de efectos adversos como fue el abuso de drogas ilegales,como la heroína. Y por el otro, un incremento en la violencia por la couta del mercado de la heroína entre las organizaciones criminales, como establece Sobrino, F. (2019), en México.
DATOS
Se construyó un panel de datos a nivel municipal, que para efectos de este trabajo se realizó el análisis exploratorio a nivel entidad federativa. Los datos son públicos: -Defunciones, los cuales fueron extraídos de lo Registros Administrativos del Instituto Nacional de Estadística y Geografía. -Incautaciones, son parte del México Unido contra la Delincuencia. -Precios internacionales de la droga, son de UNODC.
rm(list = ls())
## Directorio de trabajo
setwd("/Users/karlacruz/Desktop/final_project/data_bases/result")
base <-read_dta("finaldataENT.dta")
#modelo <-read_dta("finaldataMUN.dta")
head(base)## # A tibble: 6 × 97
## anio_ocur cve_ent ent nom_ent homicide homicide_man homicide_woman homiguns
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1990 22 22 Queréta… 0 0 0 0
## 2 1990 2 2 Baja Ca… 2 2 0 1
## 3 1990 9 9 Ciudad … 0 0 0 0
## 4 1990 8 8 Chihuah… 3 3 0 2
## 5 1990 21 21 Puebla 0 0 0 0
## 6 1990 4 4 Campeche 0 0 0 0
## # ℹ 89 more variables: homigunsman <dbl>, homigunswoman <dbl>,
## # AmpFen_SEDENA <dbl>, AseFen_SEDENA <dbl>, AseCoc_SEDENA <dbl>,
## # AseGomOpio_SEDENA <dbl>, AseHer_SEDENA <dbl>, AseMar_SEDENA <dbl>,
## # AseMet_SEDENA <dbl>, HecAma_fum_SEDENA <dbl>, HecAma_man_SEDENA <dbl>,
## # HecMar_fum_SEDENA <dbl>, HecMar_man_SEDENA <dbl>, IncCoc_SEDENA <dbl>,
## # IncGomOpio_SEDENA <dbl>, IncHer_SEDENA <dbl>, IncMar_SEDENA <dbl>,
## # IncMet_SEDENA <dbl>, IncSemAma_SEDENA <dbl>, IncSemMar_SEDENA <dbl>, …summary(base)## anio_ocur cve_ent ent nom_ent
## Min. :1990 Min. : 1.00 Length:1056 Length:1056
## 1st Qu.:1998 1st Qu.: 8.75 Class :character Class :character
## Median :2006 Median :16.50 Mode :character Mode :character
## Mean :2006 Mean :16.50
## 3rd Qu.:2014 3rd Qu.:24.25
## Max. :2022 Max. :32.00
##
## homicide homicide_man homicide_woman homiguns
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.0
## 1st Qu.: 26.75 1st Qu.: 22.0 1st Qu.: 3.00 1st Qu.: 8.0
## Median : 176.00 Median : 156.5 Median : 23.00 Median : 93.0
## Mean : 509.13 Mean : 449.3 Mean : 56.78 Mean : 327.6
## 3rd Qu.: 635.00 3rd Qu.: 568.5 3rd Qu.: 68.00 3rd Qu.: 410.2
## Max. :7886.00 Max. :6362.0 Max. :1056.00 Max. :6004.0
##
## homigunsman homigunswoman AmpFen_SEDENA AseFen_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.0000 Min. : 0.00
## 1st Qu.: 7.0 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.00
## Median : 86.0 Median : 7.00 Median : 0.0000 Median : 0.00
## Mean : 300.1 Mean : 26.89 Mean : 0.6903 Mean : 1.37
## 3rd Qu.: 378.5 3rd Qu.: 29.25 3rd Qu.: 0.0000 3rd Qu.: 0.00
## Max. :5188.0 Max. :804.00 Max. :521.0000 Max. :286.05
##
## AseCoc_SEDENA AseGomOpio_SEDENA AseHer_SEDENA AseMar_SEDENA
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.000 Median : 0.000 Median : 0.00 Median : 29.98
## Mean : 60.187 Mean : 8.254 Mean : 2.97 Mean : 5339.53
## 3rd Qu.: 1.055 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 2176.59
## Max. :4711.415 Max. :995.399 Max. :280.74 Max. :275652.79
##
## AseMet_SEDENA HecAma_fum_SEDENA HecAma_man_SEDENA HecMar_fum_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.000 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.0
## Median : 0.0 Median : 0.00 Median : 0.000 Median : 0.0
## Mean : 191.5 Mean : 55.95 Mean : 338.199 Mean : 61.6
## 3rd Qu.: 0.0 3rd Qu.: 0.00 3rd Qu.: 9.056 3rd Qu.: 0.0
## Max. :29675.9 Max. :2178.34 Max. :9494.069 Max. :3594.7
##
## HecMar_man_SEDENA IncCoc_SEDENA IncGomOpio_SEDENA IncHer_SEDENA
## Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. :0.000000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.000000
## Median : 3.703 Median : 0.000 Median : 0.0000 Median :0.000000
## Mean : 348.035 Mean : 0.182 Mean : 0.2108 Mean :0.003922
## 3rd Qu.: 161.418 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.:0.000000
## Max. :8012.398 Max. :70.765 Max. :17.5880 Max. :2.252000
##
## IncMar_SEDENA IncMet_SEDENA IncSemAma_SEDENA IncSemMar_SEDENA
## Min. : 0.0 Min. :0.0000000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.:0.0000000 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 11.5 Median :0.0000000 Median : 0.00 Median : 0.00
## Mean : 14978.3 Mean :0.0005115 Mean : 30.89 Mean : 144.63
## 3rd Qu.: 3173.8 3rd Qu.:0.0000000 3rd Qu.: 1.00 3rd Qu.: 30.24
## Max. :623229.3 Max. :0.1100000 Max. :2118.42 Max. :6280.11
##
## LabCoc_SEDENA LabHer_SEDENA LabMet_SEDENA PasFen_SEDENA
## Min. :0.000000 Min. :0.00000 Min. : 0.000 Min. : 0
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.: 0.000 1st Qu.: 0
## Median :0.000000 Median :0.00000 Median : 0.000 Median : 0
## Mean :0.000947 Mean :0.01989 Mean : 1.959 Mean : 7174
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.: 0.000 3rd Qu.: 0
## Max. :1.000000 Max. :3.00000 Max. :350.000 Max. :2450350
##
## PlaAma_fum_SEDENA PlaAma_man_SEDENA PlaMar_fum_SEDENA PlaMar_man_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.0 Min. : 0
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0
## Median : 0.0 Median : 0.00 Median : 0.0 Median : 31
## Mean : 380.2 Mean : 2942.79 Mean : 490.5 Mean : 3690
## 3rd Qu.: 0.0 3rd Qu.: 91.25 3rd Qu.: 0.0 3rd Qu.: 1441
## Max. :19514.0 Max. :121011.00 Max. :24691.0 Max. :91882
##
## SemAma_SEDENA SemMar_SEDENA AseCoc_SEMAR AseHer_SEMAR
## Min. : 0.000 Min. : 0.00 Min. : 0.0 Min. : 0.00000
## 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.00000
## Median : 0.000 Median : 0.00 Median : 0.0 Median : 0.00000
## Mean : 6.953 Mean : 25.49 Mean : 136.7 Mean : 0.05541
## 3rd Qu.: 0.000 3rd Qu.: 5.00 3rd Qu.: 0.0 3rd Qu.: 0.00000
## Max. :1122.052 Max. :2111.09 Max. :23365.0 Max. :33.80580
##
## AseMar_SEMAR AseMet_SEMAR M2Ama_SEMAR M2Mar_SEMAR
## Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0
## Median : 0.0 Median : 0.0 Median : 0 Median : 0
## Mean : 498.4 Mean : 97.2 Mean : 3019 Mean : 4410
## 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0
## Max. :49633.8 Max. :76020.6 Max. :1535990 Max. :1679804
##
## PlantasAma_SEMAR PlantasMar_SEMAR PlantiosAma_SEMAR PlantiosMar_SEMAR
## Min. : 0 Min. : 0 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0 Median : 0 Median : 0.000 Median : 0.000
## Mean : 62638 Mean : 72062 Mean : 1.216 Mean : 1.585
## 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :30388390 Max. :38743506 Max. :607.000 Max. :300.000
##
## SemAma_SEMAR SemMar_SEMAR pf_1 pf_2
## Min. : 0.00 Min. : 0.0000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.00 Median : 0.0000 Median : 0.000 Median : 0.000
## Mean : 61.44 Mean : 0.2225 Mean : 1.651 Mean : 1.853
## 3rd Qu.: 0.00 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :55615.00 Max. :65.0000 Max. :684.000 Max. :773.000
##
## pf_4 pf_5 pf_6 pf_7
## Min. : 0.000 Min. : 0.0 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0 Median : 0.000 Median : 0.0000
## Mean : 39.361 Mean : 13.1 Mean : 1.747 Mean : 0.2882
## 3rd Qu.: 0.159 3rd Qu.: 0.0 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :23353.372 Max. :1271.4 Max. :129.176 Max. :85.2828
##
## pf_8 pf_9 pf_10 pf_11
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.0000
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0000
## Median : 0.0 Median : 0.00 Median : 0.00 Median : 0.0000
## Mean : 1110.5 Mean : 17.06 Mean : 21.62 Mean : 0.4148
## 3rd Qu.: 112.3 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.0000
## Max. :61605.7 Max. :3212.79 Max. :7873.58 Max. :89.0000
##
## gn_1 gn_2 gn_4 gn_6
## Min. :0.000000 Min. : 0.00000 Min. : 0.000 Min. : 0.0000
## 1st Qu.:0.000000 1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
## Median :0.000000 Median : 0.00000 Median : 0.000 Median : 0.0000
## Mean :0.003788 Mean : 0.02462 Mean : 4.529 Mean : 0.2393
## 3rd Qu.:0.000000 3rd Qu.: 0.00000 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :3.000000 Max. :15.00000 Max. :1408.128 Max. :43.8430
##
## gn_8 gn_9 gn_10 gn_11
## Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. :0.000000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:0.000000
## Median : 0.00 Median : 0.00 Median : 0.000 Median :0.000000
## Mean : 69.42 Mean : 12.26 Mean : 2.991 Mean :0.001894
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.:0.000000
## Max. :8648.12 Max. :2005.21 Max. :2598.805 Max. :2.000000
##
## gn_12 gn_13 pob_men pob_wom
## Min. : 0.0000 Min. : 0.00000 Min. : 222978 Min. : 211340
## 1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.: 730338 1st Qu.: 753227
## Median : 0.0000 Median : 0.00000 Median :1296480 Median :1317132
## Mean : 0.2735 Mean : 0.09776 Mean :1685349 Mean :1752914
## 3rd Qu.: 0.0000 3rd Qu.: 0.00000 3rd Qu.:1980471 3rd Qu.:2101332
## Max. :82.4310 Max. :28.67000 Max. :8683082 Max. :9089378
##
## pob homicr hmenr hwomenr
## Min. : 434318 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 1482097 1st Qu.: 2.066 1st Qu.: 3.318 1st Qu.: 0.4069
## Median : 2613822 Median : 8.232 Median : 14.186 Median : 2.1389
## Mean : 3438263 Mean : 14.292 Mean : 25.697 Mean : 3.0404
## 3rd Qu.: 4072705 3rd Qu.: 17.119 3rd Qu.: 31.067 3rd Qu.: 3.6877
## Max. :17772460 Max. :197.005 Max. :347.275 Max. :47.7861
##
## hgunswomr hgunsmenr homiguns_r heroin_average
## Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. :230.0
## 1st Qu.: 0.0000 1st Qu.: 0.9412 1st Qu.: 0.5076 1st Qu.:265.0
## Median : 0.6018 Median : 6.9671 Median : 3.7233 Median :307.0
## Mean : 1.4797 Mean : 17.3331 Mean : 9.3127 Mean :320.3
## 3rd Qu.: 1.5435 3rd Qu.: 20.3472 3rd Qu.: 10.7959 3rd Qu.:374.0
## Max. :38.4259 Max. :296.6719 Max. :167.0961 Max. :481.0
## NA's :64
## heroin_average_inflation_justed2 heroin_average_adjusted_purity
## Min. :286.0 Min. : 634.0
## 1st Qu.:331.0 1st Qu.: 768.0
## Median :394.0 Median : 867.0
## Mean :449.9 Mean : 910.4
## 3rd Qu.:560.0 3rd Qu.: 983.0
## Max. :886.0 Max. :1561.0
## NA's :64 NA's :64
## heorin_average_adjusted_purity_i heroin_average_wholesale
## Min. : 788 Min. : 50750
## 1st Qu.: 949 1st Qu.: 57500
## Median :1077 Median : 65500
## Mean :1287 Mean : 86825
## 3rd Qu.:1404 3rd Qu.:129375
## Max. :3091 Max. :162500
## NA's :64 NA's :64
## heroin_average_inflation_adjuste cocaine_average_wholesale
## Min. : 53000 Min. :20500
## 1st Qu.: 67770 1st Qu.:26500
## Median : 86138 Median :29000
## Mean :129889 Mean :30711
## 3rd Qu.:208621 3rd Qu.:32550
## Max. :321782 Max. :48300
## NA's :64 NA's :32
## cocaine_average_wholesale_inflat cocaine_average
## Min. :26798 Min. : 64.00
## 1st Qu.:30848 1st Qu.: 72.00
## Median :33612 Median : 77.00
## Mean :43667 Mean : 83.75
## 3rd Qu.:51537 3rd Qu.: 96.50
## Max. :95643 Max. :120.00
## NA's :64 NA's :32
## cocaine_average_inflation_adjust cocaine_average_adjusted_purity
## Min. : 82.0 Min. : 93.0
## 1st Qu.: 99.0 1st Qu.:108.0
## Median :109.0 Median :127.0
## Mean :110.8 Mean :141.9
## 3rd Qu.:120.0 3rd Qu.:178.0
## Max. :158.0 Max. :221.0
## NA's :64 NA's :64
## cocaine_average_adjusted_purity_ _merge year
## Min. :118.0 Min. :1.000 Length:1056
## 1st Qu.:165.0 1st Qu.:3.000 Class :character
## Median :181.0 Median :3.000 Mode :character
## Mean :185.5 Mean :2.939
## 3rd Qu.:213.0 3rd Qu.:3.000
## Max. :274.0 Max. :3.000
## NA's :64
## incocaina inheroin carte_trasiegoC carte_trasiegoH
## Min. : 0.00 Min. : 0.000 Length:1056 Length:1056
## 1st Qu.: 0.00 1st Qu.: 0.000 Class :character Class :character
## Median : 0.09 Median : 0.000 Mode :character Mode :character
## Mean : 240.95 Mean : 5.015
## 3rd Qu.: 26.53 3rd Qu.: 0.000
## Max. :46718.38 Max. :340.560
## attach(base)h1 <- ggplot(base, aes(x=homicr))
h1 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
h2 <- ggplot(base, aes(log(x=homicr)))
h2 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 153 rows containing non-finite values (`stat_bin()`).
h3 <- ggplot(base, aes(x=homiguns_r))
h3 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
h4<- ggplot(base, aes(log(x=homiguns_r)))
h4 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 168 rows containing non-finite values (`stat_bin()`).
h5<- ggplot(base, aes(x=heroin_average_wholesale))
h5 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 64 rows containing non-finite values (`stat_bin()`).
h6<- ggplot(base, aes(log(x=heroin_average_wholesale)))
h6 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 64 rows containing non-finite values (`stat_bin()`).
h7<- ggplot(base, aes(x=inheroin))
h7 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
h8<- ggplot(base, aes(log(x=inheroin)))
h8 + geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 822 rows containing non-finite values (`stat_bin()`).
Correlación entre las variables de interés númericas
plot(inheroin, homicr , pch = 19, col = "lightblue")
abline(lm(inheroin ~ homicr), col = "red", lwd = 3)
text(paste("Correlación:", round(cor(inheroin, homicr), 2)), x = 50, y = 50)
plot(inheroin, homiguns_r , pch = 19, col = "lightblue")
abline(lm(inheroin ~ homiguns_r), col = "red", lwd = 3)
text(paste("Correlación:", round(cor(inheroin, homiguns_r), 2)), x = 50, y = 50)
plot(heroin_average_wholesale*inheroin, homicr , pch = 19, col = "lightblue")
abline(lm(heroin_average_wholesale*inheroin ~ homicr), col = "red", lwd = 3)
text(paste("Correlación:", round(cor(heroin_average_wholesale*inheroin, homicr), 2)), x = 100, y = 100)
Análisis exploratorio de las variables de interés
ggplot(base, aes(x = anio_ocur, y = homicr)) +
geom_point(fill = rgb(0, 0.5, 1, alpha = 1)) +
labs(title = "Tasa de Homicidios en México",
subtitle = "1990-2022",
caption = "El calculo responde al total de homicidios ocurridos por armas de fuego durante el año t, entre la población total durante el año t por cien mil habitantes",
tag = "Fig. 1")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) 
La violencia a través de los homicidios incrementa a partir de 1997, ¿qué sucedió en esos años?
ggplot(base, aes(x = anio_ocur, y =homicr , color = cve_ent)) +
geom_area(show.legend = FALSE) +
facet_wrap(~nom_ent , scales = "free") +
theme(strip.text = element_text(size = 6),
strip.background = element_blank()) +
labs(title = "Tasa de Homicidios por entidad federativa",
subtitle = "1990-2022",
caption = "El cálculo responde al total de homicidios ocurridos por armas de fuego durante el año t, entre la población total durante el año t por cien mil habitantes",
tag = "Fig. 2")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) 
Los niveles de violencia se generalizo en las entidades federativas, claramente un otras con mayor intensidad. Los homicidios responden en la entidad ocurrida NO en la registrada.
str(base)## tibble [1,056 × 97] (S3: tbl_df/tbl/data.frame)
## $ anio_ocur : num [1:1056] 1990 1990 1990 1990 1990 1990 1990 1990 1990 1990 ...
## ..- attr(*, "label")= chr "ANIO_OCUR"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cve_ent : num [1:1056] 22 2 9 8 21 4 5 1 31 19 ...
## ..- attr(*, "label")= chr "CVE_ENT"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ ent : chr [1:1056] "22" "2" "9" "8" ...
## ..- attr(*, "label")= chr "CVE_ENT"
## ..- attr(*, "format.stata")= chr "%9s"
## $ nom_ent : chr [1:1056] "Querétaro" "Baja California" "Ciudad de México" "Chihuahua" ...
## ..- attr(*, "label")= chr "NOM_ENT"
## ..- attr(*, "format.stata")= chr "%31s"
## $ homicide : num [1:1056] 0 2 0 3 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homicide"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homicide_man : num [1:1056] 0 2 0 3 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homicide_man"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homicide_woman : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homicide_woman"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homiguns : num [1:1056] 0 1 0 2 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homiguns"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homigunsman : num [1:1056] 0 1 0 2 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homigunsman"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homigunswoman : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) homigunswoman"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AmpFen_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AmpFen_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseFen_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseFen_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseCoc_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseCoc_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseGomOpio_SEDENA : num [1:1056] 0 0 0 2.27 0 ...
## ..- attr(*, "label")= chr "(sum) AseGomOpio_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseHer_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseHer_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseMar_SEDENA : num [1:1056] 0 0 0 100 590 ...
## ..- attr(*, "label")= chr "(sum) AseMar_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseMet_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseMet_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ HecAma_fum_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) HecAma_fum_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ HecAma_man_SEDENA : num [1:1056] 0 0 0 829.06 1.32 ...
## ..- attr(*, "label")= chr "(sum) HecAma_man_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ HecMar_fum_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) HecMar_fum_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ HecMar_man_SEDENA : num [1:1056] 0 37.84 0 786.81 8.98 ...
## ..- attr(*, "label")= chr "(sum) HecMar_man_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncCoc_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) IncCoc_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncGomOpio_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) IncGomOpio_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncHer_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) IncHer_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncMar_SEDENA : num [1:1056] 0 0 0 4818 155 ...
## ..- attr(*, "label")= chr "(sum) IncMar_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncMet_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) IncMet_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncSemAma_SEDENA : num [1:1056] 0 0 0 35 0 ...
## ..- attr(*, "label")= chr "(sum) IncSemAma_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ IncSemMar_SEDENA : num [1:1056] 0 0 0 50.3 0.6 ...
## ..- attr(*, "label")= chr "(sum) IncSemMar_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ LabCoc_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) LabCoc_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ LabHer_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) LabHer_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ LabMet_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) LabMet_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PasFen_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PasFen_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlaAma_fum_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlaAma_fum_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlaAma_man_SEDENA : num [1:1056] 0 0 0 10340 28 ...
## ..- attr(*, "label")= chr "(sum) PlaAma_man_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlaMar_fum_SEDENA : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlaMar_fum_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlaMar_man_SEDENA : num [1:1056] 0 38 0 11468 240 ...
## ..- attr(*, "label")= chr "(sum) PlaMar_man_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ SemAma_SEDENA : num [1:1056] 0 0 0 16.32 0.43 ...
## ..- attr(*, "label")= chr "(sum) SemAma_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ SemMar_SEDENA : num [1:1056] 0 0.2 0 15.4 12.7 ...
## ..- attr(*, "label")= chr "(sum) SemMar_SEDENA"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseCoc_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseCoc_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseHer_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseHer_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseMar_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseMar_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ AseMet_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) AseMet_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ M2Ama_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) M2Ama_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ M2Mar_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) M2Mar_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlantasAma_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlantasAma_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlantasMar_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlantasMar_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlantiosAma_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlantiosAma_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ PlantiosMar_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) PlantiosMar_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ SemAma_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) SemAma_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ SemMar_SEMAR : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) SemMar_SEMAR"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_1 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_1"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_2 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_2"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_4 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_4"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_5 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_5"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_6 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_6"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_7 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_7"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_8 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_8"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_9 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_9"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_10 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_10"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pf_11 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) pf_11"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_1 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_1"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_2 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_2"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_4 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_4"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_6 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_6"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_8 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_8"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_9 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_9"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_10 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_10"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_11 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_11"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_12 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_12"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ gn_13 : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "label")= chr "(sum) gn_13"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pob_men : num [1:1056] 700483 1264712 4156737 1542440 2508433 ...
## ..- attr(*, "label")= chr "(sum) pob_men"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pob_wom : num [1:1056] 734614 1228963 4484256 1535841 2661493 ...
## ..- attr(*, "label")= chr "(sum) pob_wom"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ pob : num [1:1056] 1435097 2493675 8640993 3078281 5169926 ...
## ..- attr(*, "label")= chr "(sum) pob"
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homicr : num [1:1056] 0 0.0802 0 0.0975 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ hmenr : num [1:1056] 0 0.158 0 0.194 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ hwomenr : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ hgunswomr : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ hgunsmenr : num [1:1056] 0 0.0791 0 0.1297 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ homiguns_r : num [1:1056] 0 0.0401 0 0.065 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ heroin_average : num [1:1056] 359 359 359 359 359 359 359 359 359 359 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ heroin_average_inflation_justed2: num [1:1056] 711 711 711 711 711 711 711 711 711 711 ...
## ..- attr(*, "label")= chr "heroin_average_inflation _justed2020"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ heroin_average_adjusted_purity : num [1:1056] 1561 1561 1561 1561 1561 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ heorin_average_adjusted_purity_i: num [1:1056] 3091 3091 3091 3091 3091 ...
## ..- attr(*, "label")= chr "Heorin_average_adjusted_purity_inflation"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ heroin_average_wholesale : num [1:1056] 162500 162500 162500 162500 162500 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ heroin_average_inflation_adjuste: num [1:1056] 321782 321782 321782 321782 321782 ...
## ..- attr(*, "label")= chr "heroin_average_inflation_adjusted_wholesale"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average_wholesale : num [1:1056] 48300 48300 48300 48300 48300 48300 48300 48300 48300 48300 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average_wholesale_inflat: num [1:1056] 95643 95643 95643 95643 95643 ...
## ..- attr(*, "label")= chr "cocaine_average_wholesale_inflation_adjusted"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average : num [1:1056] 80 80 80 80 80 80 80 80 80 80 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average_inflation_adjust: num [1:1056] 158 158 158 158 158 158 158 158 158 158 ...
## ..- attr(*, "label")= chr "cocaine_average_inflation_adjusted"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average_adjusted_purity : num [1:1056] 140 140 140 140 140 140 140 140 140 140 ...
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ cocaine_average_adjusted_purity_: num [1:1056] 274 274 274 274 274 274 274 274 274 274 ...
## ..- attr(*, "label")= chr "cocaine_average_adjusted_purity_inflation"
## ..- attr(*, "format.stata")= chr "%8.0g"
## $ _merge : dbl+lbl [1:1056] 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,...
## ..@ format.stata: chr "%23.0g"
## ..@ labels : Named num [1:5] 1 2 3 4 5
## .. ..- attr(*, "names")= chr [1:5] "master only (1)" "using only (2)" "matched (3)" "missing updated (4)" ...
## $ year : chr [1:1056] "1990" "1990" "1990" "1990" ...
## ..- attr(*, "format.stata")= chr "%9s"
## $ incocaina : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ inheroin : num [1:1056] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.stata")= chr "%9.0g"
## $ carte_trasiegoC : chr [1:1056] "0" "0" "0" "0" ...
## ..- attr(*, "format.stata")= chr "%9s"
## $ carte_trasiegoH : chr [1:1056] "0" "0" "0" "0" ...
## ..- attr(*, "format.stata")= chr "%9s"summary(object = base)## anio_ocur cve_ent ent nom_ent
## Min. :1990 Min. : 1.00 Length:1056 Length:1056
## 1st Qu.:1998 1st Qu.: 8.75 Class :character Class :character
## Median :2006 Median :16.50 Mode :character Mode :character
## Mean :2006 Mean :16.50
## 3rd Qu.:2014 3rd Qu.:24.25
## Max. :2022 Max. :32.00
##
## homicide homicide_man homicide_woman homiguns
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.0
## 1st Qu.: 26.75 1st Qu.: 22.0 1st Qu.: 3.00 1st Qu.: 8.0
## Median : 176.00 Median : 156.5 Median : 23.00 Median : 93.0
## Mean : 509.13 Mean : 449.3 Mean : 56.78 Mean : 327.6
## 3rd Qu.: 635.00 3rd Qu.: 568.5 3rd Qu.: 68.00 3rd Qu.: 410.2
## Max. :7886.00 Max. :6362.0 Max. :1056.00 Max. :6004.0
##
## homigunsman homigunswoman AmpFen_SEDENA AseFen_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.0000 Min. : 0.00
## 1st Qu.: 7.0 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.00
## Median : 86.0 Median : 7.00 Median : 0.0000 Median : 0.00
## Mean : 300.1 Mean : 26.89 Mean : 0.6903 Mean : 1.37
## 3rd Qu.: 378.5 3rd Qu.: 29.25 3rd Qu.: 0.0000 3rd Qu.: 0.00
## Max. :5188.0 Max. :804.00 Max. :521.0000 Max. :286.05
##
## AseCoc_SEDENA AseGomOpio_SEDENA AseHer_SEDENA AseMar_SEDENA
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.000 Median : 0.000 Median : 0.00 Median : 29.98
## Mean : 60.187 Mean : 8.254 Mean : 2.97 Mean : 5339.53
## 3rd Qu.: 1.055 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 2176.59
## Max. :4711.415 Max. :995.399 Max. :280.74 Max. :275652.79
##
## AseMet_SEDENA HecAma_fum_SEDENA HecAma_man_SEDENA HecMar_fum_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.000 Min. : 0.0
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.0
## Median : 0.0 Median : 0.00 Median : 0.000 Median : 0.0
## Mean : 191.5 Mean : 55.95 Mean : 338.199 Mean : 61.6
## 3rd Qu.: 0.0 3rd Qu.: 0.00 3rd Qu.: 9.056 3rd Qu.: 0.0
## Max. :29675.9 Max. :2178.34 Max. :9494.069 Max. :3594.7
##
## HecMar_man_SEDENA IncCoc_SEDENA IncGomOpio_SEDENA IncHer_SEDENA
## Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. :0.000000
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.:0.000000
## Median : 3.703 Median : 0.000 Median : 0.0000 Median :0.000000
## Mean : 348.035 Mean : 0.182 Mean : 0.2108 Mean :0.003922
## 3rd Qu.: 161.418 3rd Qu.: 0.000 3rd Qu.: 0.0000 3rd Qu.:0.000000
## Max. :8012.398 Max. :70.765 Max. :17.5880 Max. :2.252000
##
## IncMar_SEDENA IncMet_SEDENA IncSemAma_SEDENA IncSemMar_SEDENA
## Min. : 0.0 Min. :0.0000000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.:0.0000000 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 11.5 Median :0.0000000 Median : 0.00 Median : 0.00
## Mean : 14978.3 Mean :0.0005115 Mean : 30.89 Mean : 144.63
## 3rd Qu.: 3173.8 3rd Qu.:0.0000000 3rd Qu.: 1.00 3rd Qu.: 30.24
## Max. :623229.3 Max. :0.1100000 Max. :2118.42 Max. :6280.11
##
## LabCoc_SEDENA LabHer_SEDENA LabMet_SEDENA PasFen_SEDENA
## Min. :0.000000 Min. :0.00000 Min. : 0.000 Min. : 0
## 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.: 0.000 1st Qu.: 0
## Median :0.000000 Median :0.00000 Median : 0.000 Median : 0
## Mean :0.000947 Mean :0.01989 Mean : 1.959 Mean : 7174
## 3rd Qu.:0.000000 3rd Qu.:0.00000 3rd Qu.: 0.000 3rd Qu.: 0
## Max. :1.000000 Max. :3.00000 Max. :350.000 Max. :2450350
##
## PlaAma_fum_SEDENA PlaAma_man_SEDENA PlaMar_fum_SEDENA PlaMar_man_SEDENA
## Min. : 0.0 Min. : 0.00 Min. : 0.0 Min. : 0
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0
## Median : 0.0 Median : 0.00 Median : 0.0 Median : 31
## Mean : 380.2 Mean : 2942.79 Mean : 490.5 Mean : 3690
## 3rd Qu.: 0.0 3rd Qu.: 91.25 3rd Qu.: 0.0 3rd Qu.: 1441
## Max. :19514.0 Max. :121011.00 Max. :24691.0 Max. :91882
##
## SemAma_SEDENA SemMar_SEDENA AseCoc_SEMAR AseHer_SEMAR
## Min. : 0.000 Min. : 0.00 Min. : 0.0 Min. : 0.00000
## 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.00000
## Median : 0.000 Median : 0.00 Median : 0.0 Median : 0.00000
## Mean : 6.953 Mean : 25.49 Mean : 136.7 Mean : 0.05541
## 3rd Qu.: 0.000 3rd Qu.: 5.00 3rd Qu.: 0.0 3rd Qu.: 0.00000
## Max. :1122.052 Max. :2111.09 Max. :23365.0 Max. :33.80580
##
## AseMar_SEMAR AseMet_SEMAR M2Ama_SEMAR M2Mar_SEMAR
## Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0
## 1st Qu.: 0.0 1st Qu.: 0.0 1st Qu.: 0 1st Qu.: 0
## Median : 0.0 Median : 0.0 Median : 0 Median : 0
## Mean : 498.4 Mean : 97.2 Mean : 3019 Mean : 4410
## 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.: 0 3rd Qu.: 0
## Max. :49633.8 Max. :76020.6 Max. :1535990 Max. :1679804
##
## PlantasAma_SEMAR PlantasMar_SEMAR PlantiosAma_SEMAR PlantiosMar_SEMAR
## Min. : 0 Min. : 0 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0 Median : 0 Median : 0.000 Median : 0.000
## Mean : 62638 Mean : 72062 Mean : 1.216 Mean : 1.585
## 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :30388390 Max. :38743506 Max. :607.000 Max. :300.000
##
## SemAma_SEMAR SemMar_SEMAR pf_1 pf_2
## Min. : 0.00 Min. : 0.0000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
## Median : 0.00 Median : 0.0000 Median : 0.000 Median : 0.000
## Mean : 61.44 Mean : 0.2225 Mean : 1.651 Mean : 1.853
## 3rd Qu.: 0.00 3rd Qu.: 0.0000 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. :55615.00 Max. :65.0000 Max. :684.000 Max. :773.000
##
## pf_4 pf_5 pf_6 pf_7
## Min. : 0.000 Min. : 0.0 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 0.000 Median : 0.0 Median : 0.000 Median : 0.0000
## Mean : 39.361 Mean : 13.1 Mean : 1.747 Mean : 0.2882
## 3rd Qu.: 0.159 3rd Qu.: 0.0 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :23353.372 Max. :1271.4 Max. :129.176 Max. :85.2828
##
## pf_8 pf_9 pf_10 pf_11
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.0000
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.0000
## Median : 0.0 Median : 0.00 Median : 0.00 Median : 0.0000
## Mean : 1110.5 Mean : 17.06 Mean : 21.62 Mean : 0.4148
## 3rd Qu.: 112.3 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.0000
## Max. :61605.7 Max. :3212.79 Max. :7873.58 Max. :89.0000
##
## gn_1 gn_2 gn_4 gn_6
## Min. :0.000000 Min. : 0.00000 Min. : 0.000 Min. : 0.0000
## 1st Qu.:0.000000 1st Qu.: 0.00000 1st Qu.: 0.000 1st Qu.: 0.0000
## Median :0.000000 Median : 0.00000 Median : 0.000 Median : 0.0000
## Mean :0.003788 Mean : 0.02462 Mean : 4.529 Mean : 0.2393
## 3rd Qu.:0.000000 3rd Qu.: 0.00000 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :3.000000 Max. :15.00000 Max. :1408.128 Max. :43.8430
##
## gn_8 gn_9 gn_10 gn_11
## Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. :0.000000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.:0.000000
## Median : 0.00 Median : 0.00 Median : 0.000 Median :0.000000
## Mean : 69.42 Mean : 12.26 Mean : 2.991 Mean :0.001894
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.:0.000000
## Max. :8648.12 Max. :2005.21 Max. :2598.805 Max. :2.000000
##
## gn_12 gn_13 pob_men pob_wom
## Min. : 0.0000 Min. : 0.00000 Min. : 222978 Min. : 211340
## 1st Qu.: 0.0000 1st Qu.: 0.00000 1st Qu.: 730338 1st Qu.: 753227
## Median : 0.0000 Median : 0.00000 Median :1296480 Median :1317132
## Mean : 0.2735 Mean : 0.09776 Mean :1685349 Mean :1752914
## 3rd Qu.: 0.0000 3rd Qu.: 0.00000 3rd Qu.:1980471 3rd Qu.:2101332
## Max. :82.4310 Max. :28.67000 Max. :8683082 Max. :9089378
##
## pob homicr hmenr hwomenr
## Min. : 434318 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 1482097 1st Qu.: 2.066 1st Qu.: 3.318 1st Qu.: 0.4069
## Median : 2613822 Median : 8.232 Median : 14.186 Median : 2.1389
## Mean : 3438263 Mean : 14.292 Mean : 25.697 Mean : 3.0404
## 3rd Qu.: 4072705 3rd Qu.: 17.119 3rd Qu.: 31.067 3rd Qu.: 3.6877
## Max. :17772460 Max. :197.005 Max. :347.275 Max. :47.7861
##
## hgunswomr hgunsmenr homiguns_r heroin_average
## Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. :230.0
## 1st Qu.: 0.0000 1st Qu.: 0.9412 1st Qu.: 0.5076 1st Qu.:265.0
## Median : 0.6018 Median : 6.9671 Median : 3.7233 Median :307.0
## Mean : 1.4797 Mean : 17.3331 Mean : 9.3127 Mean :320.3
## 3rd Qu.: 1.5435 3rd Qu.: 20.3472 3rd Qu.: 10.7959 3rd Qu.:374.0
## Max. :38.4259 Max. :296.6719 Max. :167.0961 Max. :481.0
## NA's :64
## heroin_average_inflation_justed2 heroin_average_adjusted_purity
## Min. :286.0 Min. : 634.0
## 1st Qu.:331.0 1st Qu.: 768.0
## Median :394.0 Median : 867.0
## Mean :449.9 Mean : 910.4
## 3rd Qu.:560.0 3rd Qu.: 983.0
## Max. :886.0 Max. :1561.0
## NA's :64 NA's :64
## heorin_average_adjusted_purity_i heroin_average_wholesale
## Min. : 788 Min. : 50750
## 1st Qu.: 949 1st Qu.: 57500
## Median :1077 Median : 65500
## Mean :1287 Mean : 86825
## 3rd Qu.:1404 3rd Qu.:129375
## Max. :3091 Max. :162500
## NA's :64 NA's :64
## heroin_average_inflation_adjuste cocaine_average_wholesale
## Min. : 53000 Min. :20500
## 1st Qu.: 67770 1st Qu.:26500
## Median : 86138 Median :29000
## Mean :129889 Mean :30711
## 3rd Qu.:208621 3rd Qu.:32550
## Max. :321782 Max. :48300
## NA's :64 NA's :32
## cocaine_average_wholesale_inflat cocaine_average
## Min. :26798 Min. : 64.00
## 1st Qu.:30848 1st Qu.: 72.00
## Median :33612 Median : 77.00
## Mean :43667 Mean : 83.75
## 3rd Qu.:51537 3rd Qu.: 96.50
## Max. :95643 Max. :120.00
## NA's :64 NA's :32
## cocaine_average_inflation_adjust cocaine_average_adjusted_purity
## Min. : 82.0 Min. : 93.0
## 1st Qu.: 99.0 1st Qu.:108.0
## Median :109.0 Median :127.0
## Mean :110.8 Mean :141.9
## 3rd Qu.:120.0 3rd Qu.:178.0
## Max. :158.0 Max. :221.0
## NA's :64 NA's :64
## cocaine_average_adjusted_purity_ _merge year
## Min. :118.0 Min. :1.000 Length:1056
## 1st Qu.:165.0 1st Qu.:3.000 Class :character
## Median :181.0 Median :3.000 Mode :character
## Mean :185.5 Mean :2.939
## 3rd Qu.:213.0 3rd Qu.:3.000
## Max. :274.0 Max. :3.000
## NA's :64
## incocaina inheroin carte_trasiegoC carte_trasiegoH
## Min. : 0.00 Min. : 0.000 Length:1056 Length:1056
## 1st Qu.: 0.00 1st Qu.: 0.000 Class :character Class :character
## Median : 0.09 Median : 0.000 Mode :character Mode :character
## Mean : 240.95 Mean : 5.015
## 3rd Qu.: 26.53 3rd Qu.: 0.000
## Max. :46718.38 Max. :340.560
## dim(base)## [1] 1056 97Se construyó una base de 97 variables.
newggslopegraph(base, year, hwomenr, nom_ent,
Title = "Evolución de tasa de homicidios por cada cien mil mujeres",
SubTitle = "Entidad Federativas, 1990-2022",
Caption = "R CHARTS",
XTextSize = 7, # Tamaño textos eje X
YTextSize = 3, # Tamaño grupos
TitleTextSize = 7, # Tamaño título
SubTitleTextSize = 6, # Tamaño subtítulo
CaptionTextSize = 7, # Tamaño caption
TitleJustify = "right", # Justificado título
SubTitleJustify = "right", # Justificado subtítulo
CaptionJustify = "left", # Justificado caption
DataTextSize = 1) # Tamaño de los valores## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
newggslopegraph(base, year, hgunswomr, nom_ent,
Title = "Evolución de tasa de homicidios por cada cien mil mujeres con armas de fuego",
SubTitle = "Entidad Federativas, 1990-2022",
Caption = "R CHARTS",
XTextSize = 7, # Tamaño textos eje X
YTextSize = 3, # Tamaño grupos
TitleTextSize = 7, # Tamaño título
SubTitleTextSize = 6, # Tamaño subtítulo
CaptionTextSize = 7, # Tamaño caption
TitleJustify = "right", # Justificado título
SubTitleJustify = "right", # Justificado subtítulo
CaptionJustify = "left", # Justificado caption
DataTextSize = 1) # Tamaño de los valores## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
newggslopegraph(base, year, hmenr, nom_ent,
Title = "Evolución de tasa de homicidios por cada cien mil hombres",
SubTitle = "Entidad Federativas, 1990-2022",
Caption = "R CHARTS",
XTextSize = 7, # Tamaño textos eje X
YTextSize = 3, # Tamaño grupos
TitleTextSize = 7, # Tamaño título
SubTitleTextSize = 6, # Tamaño subtítulo
CaptionTextSize = 7, # Tamaño caption
TitleJustify = "right", # Justificado título
SubTitleJustify = "right", # Justificado subtítulo
CaptionJustify = "left", # Justificado caption
DataTextSize = 1) # Tamaño de los valores## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 8 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
newggslopegraph(base, year, hgunsmenr, nom_ent,
Title = "Evolución de tasa de homicidios por cada cien mil hombres con armas de fuego",
SubTitle = "Entidad Federativas, 1990-2022",
Caption = "R CHARTS",
XTextSize = 7, # Tamaño textos eje X
YTextSize = 3, # Tamaño grupos
TitleTextSize = 7, # Tamaño título
SubTitleTextSize = 6, # Tamaño subtítulo
CaptionTextSize = 7, # Tamaño caption
TitleJustify = "right", # Justificado título
SubTitleJustify = "right", # Justificado subtítulo
CaptionJustify = "left", # Justificado caption
DataTextSize = 1) # Tamaño de los valores## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 12 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Precios de drogas
ggplot(base, aes(x =anio_ocur, y =heroin_average_wholesale)) +
geom_area(alpha = 0.5)+
labs(title = "Precios de heroina por mayoreo en Estados Unidos de América",
subtitle = "1990-2021",
caption = "Los datos pueden ser consultados en https://dataunodc.un.org/dp-drug-prices",
tag = "Fig. 4")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) ## Warning: Removed 64 rows containing non-finite values (`stat_align()`).
ggplot(base, aes(x =anio_ocur, y =cocaine_average_wholesale)) +
geom_area(alpha = 0.5)+
labs(title = "Precios de Cocaina por mayoreo en Estados Unidos de América",
subtitle = "1990-2021",
caption = "Los datos pueden ser consultados en https://dataunodc.un.org/dp-drug-prices",
tag = "Fig. 4")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) ## Warning: Removed 32 rows containing non-finite values (`stat_align()`).
Incautaciones en México
ggplot(base, aes(x = anio_ocur, y =incocaina , color = cve_ent)) +
geom_area(show.legend = FALSE) +
facet_wrap(~nom_ent , scales = "free") +
theme(strip.text = element_text(size = 6),
strip.background = element_blank()) +
labs(title = "Kilogramos de cocaina incautados por entidad federativa",
subtitle = "1990-2022",
caption = "",
tag = "Fig. 8")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) 
ggplot(base, aes(x = anio_ocur, y =inheroin , color = cve_ent)) +
geom_area(show.legend = FALSE) +
facet_wrap(~nom_ent , scales = "free") +
theme(strip.text = element_text(size = 6),
strip.background = element_blank()) +
labs(title = "Kilogramos de heroina incautados por entidad federativa",
subtitle = "1990-2022",
caption = "",
tag = "Fig. 8")+
theme(plot.caption.position = "plot",
plot.caption = element_text(hjust = 0)) 
##Método
Para efectos de esta investigación se realizo un modelo de panel de datos con efectos fijos y aleatorio (Sin logaritmos)
modelo_pool <- plm(homiguns_r ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="pooling")
summary(modelo_pool)## Pooling Model
##
## Call:
## plm(formula = homiguns_r ~ carte_trasiegoH * heroin_average_wholesale,
## data = base, model = "pooling")
##
## Balanced Panel: n = 31, T = 32, N = 992
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -20.2302 -5.3758 -1.0687 1.0800 136.8617
##
## Coefficients:
## Estimate Std. Error t-value
## (Intercept) 1.3366e+01 1.0648e+00 12.5525
## carte_trasiegoH1 2.0320e+01 3.2383e+00 6.2751
## heroin_average_wholesale -8.8898e-05 1.0448e-05 -8.5085
## carte_trasiegoH1:heroin_average_wholesale -1.4490e-04 4.8215e-05 -3.0053
## Pr(>|t|)
## (Intercept) < 2.2e-16 ***
## carte_trasiegoH1 5.221e-10 ***
## heroin_average_wholesale < 2.2e-16 ***
## carte_trasiegoH1:heroin_average_wholesale 0.00272 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 181410
## Residual Sum of Squares: 134860
## R-Squared: 0.2566
## Adj. R-Squared: 0.25434
## F-statistic: 113.675 on 3 and 988 DF, p-value: < 2.22e-16modelo_fe <- plm(homiguns_r ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="within")
summary(modelo_fe)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = homiguns_r ~ carte_trasiegoH * heroin_average_wholesale,
## data = base, model = "within")
##
## Balanced Panel: n = 31, T = 32, N = 992
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -24.076324 -3.885631 -0.041384 0.570211 129.784309
##
## Coefficients:
## Estimate Std. Error t-value
## carte_trasiegoH1 1.4222e+01 3.2851e+00 4.3291
## carte_trasiegoH1:heroin_average_wholesale -7.2230e-05 4.8344e-05 -1.4941
## Pr(>|t|)
## carte_trasiegoH1 1.654e-05 ***
## carte_trasiegoH1:heroin_average_wholesale 0.1355
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 132220
## Residual Sum of Squares: 119610
## R-Squared: 0.095356
## Adj. R-Squared: 0.06517
## F-statistic: 50.5428 on 2 and 959 DF, p-value: < 2.22e-16pFtest(modelo_fe, modelo_pool)##
## F test for individual effects
##
## data: homiguns_r ~ carte_trasiegoH * heroin_average_wholesale
## F = 4.2179, df1 = 29, df2 = 959, p-value = 1.757e-12
## alternative hypothesis: significant effectsPara un modelo con efectos aleatorios
wallace<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "walhus" )
summary(wallace)## Oneway (individual) effect Random Effect Model
## (Wallace-Hussain's transformation)
##
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale,
## data = base, model = "random", random.method = "walhus")
##
## Balanced Panel: n = 31, T = 32, N = 992
##
## Effects:
## var std.dev share
## idiosyncratic 193.756 13.920 0.907
## individual 19.874 4.458 0.093
## theta: 0.5168
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -29.44837 -6.05228 -0.70123 0.80287 155.43359
##
## Coefficients:
## Estimate Std. Error z-value
## (Intercept) 2.3074e+01 2.3574e+00 9.7879
## carte_trasiegoH1 2.0942e+01 4.0383e+00 5.1857
## heroin_average_wholesale -1.5239e-04 2.4288e-05 -6.2744
## carte_trasiegoH1:heroin_average_wholesale -1.2996e-04 5.9604e-05 -2.1803
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## carte_trasiegoH1 2.152e-07 ***
## heroin_average_wholesale 3.509e-10 ***
## carte_trasiegoH1:heroin_average_wholesale 0.02924 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 228820
## Residual Sum of Squares: 191760
## R-Squared: 0.16198
## Adj. R-Squared: 0.15943
## Chisq: 190.967 on 3 DF, p-value: < 2.22e-16ame<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "amemiya" )
summary(ame)## Oneway (individual) effect Random Effect Model
## (Amemiya's transformation)
##
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale,
## data = base, model = "random", random.method = "amemiya")
##
## Balanced Panel: n = 31, T = 32, N = 992
##
## Effects:
## var std.dev share
## idiosyncratic 192.930 13.890 0.771
## individual 57.390 7.576 0.229
## theta: 0.6917
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -30.18818 -5.35439 -0.45234 0.66731 153.97228
##
## Coefficients:
## Estimate Std. Error z-value
## (Intercept) 2.3278e+01 3.5667e+00 6.5265
## carte_trasiegoH1 1.9855e+01 4.0333e+00 4.9228
## heroin_average_wholesale -1.5398e-04 3.7198e-05 -4.1396
## carte_trasiegoH1:heroin_average_wholesale -1.1739e-04 5.9427e-05 -1.9754
## Pr(>|z|)
## (Intercept) 6.731e-11 ***
## carte_trasiegoH1 8.533e-07 ***
## heroin_average_wholesale 3.479e-05 ***
## carte_trasiegoH1:heroin_average_wholesale 0.04822 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 215110
## Residual Sum of Squares: 188000
## R-Squared: 0.12602
## Adj. R-Squared: 0.12337
## Chisq: 142.459 on 3 DF, p-value: < 2.22e-16ner<- plm(homicr ~ carte_trasiegoH*heroin_average_wholesale, data=base, model="random", random.method = "nerlove" )
summary(wallace)## Oneway (individual) effect Random Effect Model
## (Wallace-Hussain's transformation)
##
## Call:
## plm(formula = homicr ~ carte_trasiegoH * heroin_average_wholesale,
## data = base, model = "random", random.method = "walhus")
##
## Balanced Panel: n = 31, T = 32, N = 992
##
## Effects:
## var std.dev share
## idiosyncratic 193.756 13.920 0.907
## individual 19.874 4.458 0.093
## theta: 0.5168
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -29.44837 -6.05228 -0.70123 0.80287 155.43359
##
## Coefficients:
## Estimate Std. Error z-value
## (Intercept) 2.3074e+01 2.3574e+00 9.7879
## carte_trasiegoH1 2.0942e+01 4.0383e+00 5.1857
## heroin_average_wholesale -1.5239e-04 2.4288e-05 -6.2744
## carte_trasiegoH1:heroin_average_wholesale -1.2996e-04 5.9604e-05 -2.1803
## Pr(>|z|)
## (Intercept) < 2.2e-16 ***
## carte_trasiegoH1 2.152e-07 ***
## heroin_average_wholesale 3.509e-10 ***
## carte_trasiegoH1:heroin_average_wholesale 0.02924 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 228820
## Residual Sum of Squares: 191760
## R-Squared: 0.16198
## Adj. R-Squared: 0.15943
## Chisq: 190.967 on 3 DF, p-value: < 2.22e-16phtest(wallace, modelo_fe)##
## Hausman Test
##
## data: homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 21.824, df = 2, p-value = 1.824e-05
## alternative hypothesis: one model is inconsistentphtest(ner, modelo_fe)##
## Hausman Test
##
## data: homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 17.02, df = 2, p-value = 0.0002015
## alternative hypothesis: one model is inconsistentphtest(ame, modelo_fe)##
## Hausman Test
##
## data: homicr ~ carte_trasiegoH * heroin_average_wholesale
## chisq = 17.417, df = 2, p-value = 0.0001652
## alternative hypothesis: one model is inconsistent##COMENTARIOS FINALES
-Los niveles de violencia han incrementado durante 1997.
-La tasa de homicidios máxima es 197 homicidios por cada cien mil habitantes. La media responde14 homicidios.
Datos diferenciados:
-La tasa de homicidios de los hombres durante 1990-2022 oscila en 347 homicidios por cada cien mil habitantes. La media es 25 homicidios.
-Para el caso diferenciado de las mujeres, tasa máxima es 47 homicidios por cada cien mi mujeres habitantes. Mientras su media es 3 homicidios.
-Precio de droga varia durante el tiempo. Anteriormente, el precio era mayor.
-El valor máximo de la heroína 162,500 dls sin ajustarse a la inflación
-Existe débil correlación minima entre homicidios y la presencia de drogas. -Se realizó una serie de modelos con efectos fijos y aleatorios para conocer la relación entre variables; sin embargo, no se ahondo en el análisis de coeficientes.
REFERENCIAS
[1] Angrist, J. D. and Kugler, A. D. (2008). Rural Windfall or a New Resource Curse? Coca, Income, and Civil Conflict in Colombia. The Review of Economics and Statistics 90(2), 191-215. [2] Acemoglu, D., De Feo, G., and De Luca, G. D. (2020).Weak states: Causes and consequences of the Sicilian Mafia . The Review of Economic Studies, 87(2), 537- 581. [3] Astorga, L. (2005). El Siglo de las Drogas. México: Plaza y Janés. [4] Bejarano, R. (2018). Competencia electoral, redes de protección y violencia del crimen organizado en México. 2006-2016. [5] Biderman, C., Mello, J. M., De Lima, R. S. D. and Schneider, A. (2018). Pax Monopolista and Crime: The Case of the Emergence of the Primeiro Comando da Capital in São Paulo. Journal of Quantitative Criminology 35(3), 573-605. [6] Bounadi, M. E. (2018). Weather and Conflicts in Afghanistan. EBA Working Paper, Expert Group for Aid Studies, Sweden. [7] Brown, R. H. (2002). The Opium Trade and Opium Policies in India, China, Britain, and the United States: Historical Comparisons and Theoretical Interpretations. Asian Journal of Social Science, Vol. 30, No. 3, pp. 623-656. [8] DEA (2019). 2019 National Drug Threat Assessment, Technical Report. U.S. Department of Justice Drug Enforcement Administration. [9] De Sagrera, J. E. (2005). El opio. Elservier.Vol.24. Núm.10. pp. 77-110. [10] Coscia, M. and Rios, V. (2012). Knowing where and how Criminal Organizations Operate using Web Content, Proceedings of the 21st ACM international conference on Information and knowledge management. pp. 1412-1421.